=====================
MCMCSamplesDict class
=====================
`pesummary` handles multiple MCMC chains through a custom `MCMCSamplesDict`
class. This `MCMCSamplesDict` class is inhertied from the core `dict` class and
stores multiple `SamplesDict` objects, one for each chain. See
`SanplesDict class <SamplesDict.html>`_ for more information. This
`MCMCSamplesDict` class has many useful properties, for example working out the
Gelman-Rubin statistic.
Initializing the MCMCSamplesDict class
--------------------------------------
The `MCMCSamplesDict` class is initialized with an array of parameters and an
array containing the samples for each parameter for each chain,
.. code-block:: python
>>> from pesummary.utils.samples_dict import MCMCSamplesDict
>>> parameters = ["a", "b"]
>>> samples = [
... [
... [1, 1.2, 1.7, 1.1, 1.4, 0.8, 1.6],
... [10.2, 11.3, 11.6, 9.5, 8.6, 10.8, 10.9]
... ], [
... [0.8, 0.5, 1.7, 1.4, 1.2, 1.7, 0.9],
... [10, 10.5, 10.4, 9.6, 8.6, 11.6, 16.2]
... ]
... ]
>>> dataset = MCMCSamplesDict(parameter, samples)
>>> print(dataset.keys())
dict_keys(['chain_0', 'chain_1'])
>>> print(dataset["chain_0"])
idx a b
0 1.000000 10.200000
1 1.200000 11.300000
2 1.700000 11.600000
3 1.100000 9.500000
4 1.400000 8.600000
5 0.800000 10.800000
6 1.600000 10.900000
Using the MCMCSamplesDict properties
------------------------------------
Below we show some of the useful properties of the `MCMCSamplesDict` class. For
full details see the doc string,
.. code-block:: python
>>> dataset.nchains
2
>>> transpose = dataset.T
>>> transpose["a"]
idx chain_0 chain_1
0 1.000000 0.800000
1 1.200000 0.500000
2 1.700000 1.700000
3 1.100000 1.400000
4 1.400000 1.200000
5 0.800000 1.700000
6 1.600000 0.900000
>>> dataset.average
idx a b
0 0.900000 10.100000
1 0.850000 10.900000
2 1.700000 11.000000
3 1.250000 9.550000
4 1.300000 8.600000
5 1.250000 11.200000
6 1.250000 13.550000
Using the MCMCSamplesDict function
------------------------------------
Below we show some of the useful function of the `MCMCSamplesDict` class. For
full details see the doc string,
.. code-block:: python
>>> dataset.gelman_rubin("a")
1.02018